Overview

Dataset statistics

Number of variables24
Number of observations29965
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.7 MiB
Average record size in memory200.0 B

Variable types

Numeric21
Categorical3

Alerts

PAY_0 is highly overall correlated with PAY_2 and 2 other fieldsHigh correlation
PAY_2 is highly overall correlated with PAY_0 and 7 other fieldsHigh correlation
PAY_3 is highly overall correlated with PAY_0 and 9 other fieldsHigh correlation
PAY_4 is highly overall correlated with PAY_0 and 10 other fieldsHigh correlation
PAY_5 is highly overall correlated with PAY_2 and 8 other fieldsHigh correlation
PAY_6 is highly overall correlated with PAY_2 and 8 other fieldsHigh correlation
BILL_AMT1 is highly overall correlated with PAY_2 and 8 other fieldsHigh correlation
BILL_AMT2 is highly overall correlated with PAY_2 and 10 other fieldsHigh correlation
BILL_AMT3 is highly overall correlated with PAY_2 and 11 other fieldsHigh correlation
BILL_AMT4 is highly overall correlated with PAY_3 and 13 other fieldsHigh correlation
BILL_AMT5 is highly overall correlated with PAY_3 and 13 other fieldsHigh correlation
BILL_AMT6 is highly overall correlated with PAY_4 and 11 other fieldsHigh correlation
PAY_AMT1 is highly overall correlated with BILL_AMT1 and 5 other fieldsHigh correlation
PAY_AMT2 is highly overall correlated with BILL_AMT3 and 5 other fieldsHigh correlation
PAY_AMT3 is highly overall correlated with BILL_AMT4 and 7 other fieldsHigh correlation
PAY_AMT4 is highly overall correlated with BILL_AMT4 and 6 other fieldsHigh correlation
PAY_AMT5 is highly overall correlated with BILL_AMT4 and 5 other fieldsHigh correlation
PAY_AMT6 is highly overall correlated with BILL_AMT5 and 4 other fieldsHigh correlation
PAY_0 has 14737 (49.2%) zerosZeros
PAY_2 has 15730 (52.5%) zerosZeros
PAY_3 has 15764 (52.6%) zerosZeros
PAY_4 has 16455 (54.9%) zerosZeros
PAY_5 has 16947 (56.6%) zerosZeros
PAY_6 has 16286 (54.4%) zerosZeros
PAY_AMT1 has 5218 (17.4%) zerosZeros
PAY_AMT2 has 5365 (17.9%) zerosZeros
PAY_AMT3 has 5937 (19.8%) zerosZeros
PAY_AMT4 has 6377 (21.3%) zerosZeros
PAY_AMT5 has 6672 (22.3%) zerosZeros
PAY_AMT6 has 7142 (23.8%) zerosZeros

Reproduction

Analysis started2023-03-02 14:15:46.937958
Analysis finished2023-03-02 14:18:36.440135
Duration2 minutes and 49.5 seconds
Software versionydata-profiling vv4.0.0
Download configurationconfig.json

Variables

LIMIT_BAL
Real number (ℝ)

Distinct81
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.662581
Minimum9.2104404
Maximum13.815512
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size468.2 KiB
2023-03-02T15:18:36.767871image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum9.2104404
5-th percentile9.9035376
Q110.819798
median11.849405
Q312.388398
95-th percentile12.971543
Maximum13.815512
Range4.6050712
Interquartile range (IQR)1.5686001

Descriptive statistics

Standard deviation0.94129863
Coefficient of variation (CV)0.080711006
Kurtosis-0.51172171
Mean11.662581
Median Absolute Deviation (MAD)0.69314004
Skewness-0.5127289
Sum349469.24
Variance0.88604312
MonotonicityNot monotonic
2023-03-02T15:18:37.397186image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10.81979828 3363
 
11.2%
9.903537551 1975
 
6.6%
10.30898599 1610
 
5.4%
11.28979441 1564
 
5.2%
12.20607765 1524
 
5.1%
11.91839724 1107
 
3.7%
11.51293546 1047
 
3.5%
12.10071769 993
 
3.3%
12.79386209 874
 
2.9%
11.00211651 825
 
2.8%
Other values (71) 15083
50.3%
ValueCountFrequency (%)
9.210440367 493
 
1.6%
9.680406499 2
 
< 0.1%
9.903537551 1975
6.6%
10.30898599 1610
5.4%
10.59665973 230
 
0.8%
10.81979828 3363
11.2%
11.00211651 825
 
2.8%
11.15626481 731
 
2.4%
11.28979441 1564
5.2%
11.40757606 650
 
2.2%
ValueCountFrequency (%)
13.81551156 1
 
< 0.1%
13.59236826 2
 
< 0.1%
13.56705048 2
 
< 0.1%
13.54107503 1
 
< 0.1%
13.52782982 4
< 0.1%
13.51440682 2
 
< 0.1%
13.50080118 2
 
< 0.1%
13.48700788 3
 
< 0.1%
13.47302166 6
< 0.1%
13.45883704 8
< 0.1%

SEX
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size468.2 KiB
2
18091 
1
11874 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters29965
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row1

Common Values

ValueCountFrequency (%)
2 18091
60.4%
1 11874
39.6%

Length

2023-03-02T15:18:38.259024image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-02T15:18:39.365855image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
2 18091
60.4%
1 11874
39.6%

Most occurring characters

ValueCountFrequency (%)
2 18091
60.4%
1 11874
39.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 29965
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 18091
60.4%
1 11874
39.6%

Most occurring scripts

ValueCountFrequency (%)
Common 29965
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 18091
60.4%
1 11874
39.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 29965
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 18091
60.4%
1 11874
39.6%

EDUCATION
Real number (ℝ)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.8536292
Minimum0
Maximum6
Zeros14
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size468.2 KiB
2023-03-02T15:18:40.440599image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median2
Q32
95-th percentile3
Maximum6
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.79041147
Coefficient of variation (CV)0.42641293
Kurtosis2.079207
Mean1.8536292
Median Absolute Deviation (MAD)1
Skewness0.97070927
Sum55544
Variance0.62475029
MonotonicityNot monotonic
2023-03-02T15:18:41.748024image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
2 14019
46.8%
1 10563
35.3%
3 4915
 
16.4%
5 280
 
0.9%
4 123
 
0.4%
6 51
 
0.2%
0 14
 
< 0.1%
ValueCountFrequency (%)
0 14
 
< 0.1%
1 10563
35.3%
2 14019
46.8%
3 4915
 
16.4%
4 123
 
0.4%
5 280
 
0.9%
6 51
 
0.2%
ValueCountFrequency (%)
6 51
 
0.2%
5 280
 
0.9%
4 123
 
0.4%
3 4915
 
16.4%
2 14019
46.8%
1 10563
35.3%
0 14
 
< 0.1%

MARRIAGE
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size468.2 KiB
2
15945 
1
13643 
3
 
323
0
 
54

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters29965
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row2
3rd row2
4th row1
5th row1

Common Values

ValueCountFrequency (%)
2 15945
53.2%
1 13643
45.5%
3 323
 
1.1%
0 54
 
0.2%

Length

2023-03-02T15:18:43.083186image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-02T15:18:44.601255image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
2 15945
53.2%
1 13643
45.5%
3 323
 
1.1%
0 54
 
0.2%

Most occurring characters

ValueCountFrequency (%)
2 15945
53.2%
1 13643
45.5%
3 323
 
1.1%
0 54
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 29965
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 15945
53.2%
1 13643
45.5%
3 323
 
1.1%
0 54
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common 29965
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 15945
53.2%
1 13643
45.5%
3 323
 
1.1%
0 54
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 29965
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 15945
53.2%
1 13643
45.5%
3 323
 
1.1%
0 54
 
0.2%

AGE
Real number (ℝ)

Distinct56
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.487969
Minimum21
Maximum79
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size468.2 KiB
2023-03-02T15:18:46.459796image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum21
5-th percentile23
Q128
median34
Q341
95-th percentile53
Maximum79
Range58
Interquartile range (IQR)13

Descriptive statistics

Standard deviation9.2194592
Coefficient of variation (CV)0.25979112
Kurtosis0.043988015
Mean35.487969
Median Absolute Deviation (MAD)6
Skewness0.732056
Sum1063397
Variance84.998429
MonotonicityNot monotonic
2023-03-02T15:18:47.779459image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
29 1602
 
5.3%
27 1475
 
4.9%
28 1406
 
4.7%
30 1394
 
4.7%
26 1252
 
4.2%
31 1213
 
4.0%
25 1185
 
4.0%
34 1161
 
3.9%
32 1157
 
3.9%
33 1146
 
3.8%
Other values (46) 16974
56.6%
ValueCountFrequency (%)
21 67
 
0.2%
22 560
 
1.9%
23 930
3.1%
24 1126
3.8%
25 1185
4.0%
26 1252
4.2%
27 1475
4.9%
28 1406
4.7%
29 1602
5.3%
30 1394
4.7%
ValueCountFrequency (%)
79 1
 
< 0.1%
75 3
 
< 0.1%
74 1
 
< 0.1%
73 4
 
< 0.1%
72 3
 
< 0.1%
71 3
 
< 0.1%
70 10
< 0.1%
69 15
0.1%
68 5
 
< 0.1%
67 16
0.1%

PAY_0
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.016752878
Minimum-2
Maximum8
Zeros14737
Zeros (%)49.2%
Negative8432
Negative (%)28.1%
Memory size468.2 KiB
2023-03-02T15:18:49.533547image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum-2
5-th percentile-2
Q1-1
median0
Q30
95-th percentile2
Maximum8
Range10
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.123492
Coefficient of variation (CV)-67.062627
Kurtosis2.7300384
Mean-0.016752878
Median Absolute Deviation (MAD)1
Skewness0.73460648
Sum-502
Variance1.2622343
MonotonicityNot monotonic
2023-03-02T15:18:50.832968image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 14737
49.2%
-1 5682
 
19.0%
1 3667
 
12.2%
-2 2750
 
9.2%
2 2666
 
8.9%
3 322
 
1.1%
4 76
 
0.3%
5 26
 
0.1%
8 19
 
0.1%
6 11
 
< 0.1%
ValueCountFrequency (%)
-2 2750
 
9.2%
-1 5682
 
19.0%
0 14737
49.2%
1 3667
 
12.2%
2 2666
 
8.9%
3 322
 
1.1%
4 76
 
0.3%
5 26
 
0.1%
6 11
 
< 0.1%
7 9
 
< 0.1%
ValueCountFrequency (%)
8 19
 
0.1%
7 9
 
< 0.1%
6 11
 
< 0.1%
5 26
 
0.1%
4 76
 
0.3%
3 322
 
1.1%
2 2666
 
8.9%
1 3667
 
12.2%
0 14737
49.2%
-1 5682
 
19.0%

PAY_2
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.13185383
Minimum-2
Maximum8
Zeros15730
Zeros (%)52.5%
Negative9798
Negative (%)32.7%
Memory size468.2 KiB
2023-03-02T15:18:51.829347image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum-2
5-th percentile-2
Q1-1
median0
Q30
95-th percentile2
Maximum8
Range10
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1963217
Coefficient of variation (CV)-9.0730903
Kurtosis1.5776087
Mean-0.13185383
Median Absolute Deviation (MAD)0
Skewness0.79207041
Sum-3951
Variance1.4311856
MonotonicityNot monotonic
2023-03-02T15:18:52.543653image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 15730
52.5%
-1 6046
 
20.2%
2 3926
 
13.1%
-2 3752
 
12.5%
3 326
 
1.1%
4 99
 
0.3%
1 28
 
0.1%
5 25
 
0.1%
7 20
 
0.1%
6 12
 
< 0.1%
ValueCountFrequency (%)
-2 3752
 
12.5%
-1 6046
 
20.2%
0 15730
52.5%
1 28
 
0.1%
2 3926
 
13.1%
3 326
 
1.1%
4 99
 
0.3%
5 25
 
0.1%
6 12
 
< 0.1%
7 20
 
0.1%
ValueCountFrequency (%)
8 1
 
< 0.1%
7 20
 
0.1%
6 12
 
< 0.1%
5 25
 
0.1%
4 99
 
0.3%
3 326
 
1.1%
2 3926
 
13.1%
1 28
 
0.1%
0 15730
52.5%
-1 6046
 
20.2%

PAY_3
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.16439179
Minimum-2
Maximum8
Zeros15764
Zeros (%)52.6%
Negative9989
Negative (%)33.3%
Memory size468.2 KiB
2023-03-02T15:18:53.381545image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum-2
5-th percentile-2
Q1-1
median0
Q30
95-th percentile2
Maximum8
Range10
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1958775
Coefficient of variation (CV)-7.2745574
Kurtosis2.091666
Mean-0.16439179
Median Absolute Deviation (MAD)0
Skewness0.84146398
Sum-4926
Variance1.430123
MonotonicityNot monotonic
2023-03-02T15:18:54.363014image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 15764
52.6%
-1 5934
 
19.8%
-2 4055
 
13.5%
2 3819
 
12.7%
3 240
 
0.8%
4 75
 
0.3%
7 27
 
0.1%
6 23
 
0.1%
5 21
 
0.1%
1 4
 
< 0.1%
ValueCountFrequency (%)
-2 4055
 
13.5%
-1 5934
 
19.8%
0 15764
52.6%
1 4
 
< 0.1%
2 3819
 
12.7%
3 240
 
0.8%
4 75
 
0.3%
5 21
 
0.1%
6 23
 
0.1%
7 27
 
0.1%
ValueCountFrequency (%)
8 3
 
< 0.1%
7 27
 
0.1%
6 23
 
0.1%
5 21
 
0.1%
4 75
 
0.3%
3 240
 
0.8%
2 3819
 
12.7%
1 4
 
< 0.1%
0 15764
52.6%
-1 5934
 
19.8%

PAY_4
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.21892208
Minimum-2
Maximum8
Zeros16455
Zeros (%)54.9%
Negative10001
Negative (%)33.4%
Memory size468.2 KiB
2023-03-02T15:18:55.298886image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum-2
5-th percentile-2
Q1-1
median0
Q30
95-th percentile2
Maximum8
Range10
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1681752
Coefficient of variation (CV)-5.3360319
Kurtosis3.5089621
Mean-0.21892208
Median Absolute Deviation (MAD)0
Skewness1.0007986
Sum-6560
Variance1.3646333
MonotonicityNot monotonic
2023-03-02T15:18:56.038129image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 16455
54.9%
-1 5683
 
19.0%
-2 4318
 
14.4%
2 3159
 
10.5%
3 180
 
0.6%
4 68
 
0.2%
7 58
 
0.2%
5 35
 
0.1%
6 5
 
< 0.1%
1 2
 
< 0.1%
ValueCountFrequency (%)
-2 4318
 
14.4%
-1 5683
 
19.0%
0 16455
54.9%
1 2
 
< 0.1%
2 3159
 
10.5%
3 180
 
0.6%
4 68
 
0.2%
5 35
 
0.1%
6 5
 
< 0.1%
7 58
 
0.2%
ValueCountFrequency (%)
8 2
 
< 0.1%
7 58
 
0.2%
6 5
 
< 0.1%
5 35
 
0.1%
4 68
 
0.2%
3 180
 
0.6%
2 3159
 
10.5%
1 2
 
< 0.1%
0 16455
54.9%
-1 5683
 
19.0%

PAY_5
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.26450859
Minimum-2
Maximum8
Zeros16947
Zeros (%)56.6%
Negative10051
Negative (%)33.5%
Memory size468.2 KiB
2023-03-02T15:18:56.662390image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum-2
5-th percentile-2
Q1-1
median0
Q30
95-th percentile2
Maximum8
Range10
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1322199
Coefficient of variation (CV)-4.2804653
Kurtosis4.0035623
Mean-0.26450859
Median Absolute Deviation (MAD)0
Skewness1.009329
Sum-7926
Variance1.2819218
MonotonicityNot monotonic
2023-03-02T15:18:57.288309image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0 16947
56.6%
-1 5535
 
18.5%
-2 4516
 
15.1%
2 2626
 
8.8%
3 178
 
0.6%
4 83
 
0.3%
7 58
 
0.2%
5 17
 
0.1%
6 4
 
< 0.1%
8 1
 
< 0.1%
ValueCountFrequency (%)
-2 4516
 
15.1%
-1 5535
 
18.5%
0 16947
56.6%
2 2626
 
8.8%
3 178
 
0.6%
4 83
 
0.3%
5 17
 
0.1%
6 4
 
< 0.1%
7 58
 
0.2%
8 1
 
< 0.1%
ValueCountFrequency (%)
8 1
 
< 0.1%
7 58
 
0.2%
6 4
 
< 0.1%
5 17
 
0.1%
4 83
 
0.3%
3 178
 
0.6%
2 2626
 
8.8%
0 16947
56.6%
-1 5535
 
18.5%
-2 4516
 
15.1%

PAY_6
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.28943768
Minimum-2
Maximum8
Zeros16286
Zeros (%)54.4%
Negative10601
Negative (%)35.4%
Memory size468.2 KiB
2023-03-02T15:18:57.928058image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum-2
5-th percentile-2
Q1-1
median0
Q30
95-th percentile2
Maximum8
Range10
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1490901
Coefficient of variation (CV)-3.9700778
Kurtosis3.4372569
Mean-0.28943768
Median Absolute Deviation (MAD)0
Skewness0.94860899
Sum-8673
Variance1.3204081
MonotonicityNot monotonic
2023-03-02T15:18:58.528521image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0 16286
54.4%
-1 5736
 
19.1%
-2 4865
 
16.2%
2 2766
 
9.2%
3 184
 
0.6%
4 48
 
0.2%
7 46
 
0.2%
6 19
 
0.1%
5 13
 
< 0.1%
8 2
 
< 0.1%
ValueCountFrequency (%)
-2 4865
 
16.2%
-1 5736
 
19.1%
0 16286
54.4%
2 2766
 
9.2%
3 184
 
0.6%
4 48
 
0.2%
5 13
 
< 0.1%
6 19
 
0.1%
7 46
 
0.2%
8 2
 
< 0.1%
ValueCountFrequency (%)
8 2
 
< 0.1%
7 46
 
0.2%
6 19
 
0.1%
5 13
 
< 0.1%
4 48
 
0.2%
3 184
 
0.6%
2 2766
 
9.2%
0 16286
54.4%
-1 5736
 
19.1%
-2 4865
 
16.2%

BILL_AMT1
Real number (ℝ)

Distinct22723
Distinct (%)75.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean176.25262
Minimum-406.91522
Maximum982.09572
Zeros0
Zeros (%)0.0%
Negative590
Negative (%)2.0%
Memory size468.2 KiB
2023-03-02T15:18:59.294066image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum-406.91522
5-th percentile1
Q159.966657
median149.79653
Q3259.34726
95-th percentile448.67003
Maximum982.09572
Range1389.0109
Interquartile range (IQR)199.3806

Descriptive statistics

Standard deviation142.35573
Coefficient of variation (CV)0.80768007
Kurtosis0.5973453
Mean176.25262
Median Absolute Deviation (MAD)96.579201
Skewness0.90287644
Sum5281409.9
Variance20265.154
MonotonicityNot monotonic
2023-03-02T15:19:00.310963image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1978
 
6.6%
19.77371993 243
 
0.8%
27.94637722 76
 
0.3%
18.08314132 72
 
0.2%
17.80449381 63
 
0.2%
50.009999 59
 
0.2%
19.92485885 48
 
0.2%
49 39
 
0.1%
20.42057786 29
 
0.1%
22.38302929 25
 
0.1%
Other values (22713) 27333
91.2%
ValueCountFrequency (%)
-406.9152246 1
< 0.1%
-393.6661022 1
< 0.1%
-123.7255026 1
< 0.1%
-119.9416525 1
< 0.1%
-107.4476617 1
< 0.1%
-103.3537614 1
< 0.1%
-99.00505038 1
< 0.1%
-95.36770942 1
< 0.1%
-90.48204242 1
< 0.1%
-86.24384036 1
< 0.1%
ValueCountFrequency (%)
982.0957183 1
< 0.1%
864.1845868 1
< 0.1%
808.1231342 1
< 0.1%
794.0144835 1
< 0.1%
791.6116472 1
< 0.1%
788.5112555 1
< 0.1%
783.4928206 1
< 0.1%
781.4883236 1
< 0.1%
780.12499 1
< 0.1%
777.1872361 1
< 0.1%

BILL_AMT2
Real number (ℝ)

Distinct22346
Distinct (%)74.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean171.50088
Minimum-264.15336
Maximum991.93347
Zeros0
Zeros (%)0.0%
Negative669
Negative (%)2.2%
Memory size468.2 KiB
2023-03-02T15:19:00.983216image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum-264.15336
5-th percentile1
Q154.87258
median145.93149
Q3253.19953
95-th percentile441.46415
Maximum991.93347
Range1256.0868
Interquartile range (IQR)198.32695

Descriptive statistics

Standard deviation140.99789
Coefficient of variation (CV)0.8221409
Kurtosis0.57725256
Mean171.50088
Median Absolute Deviation (MAD)96.737995
Skewness0.88724829
Sum5139023.9
Variance19880.405
MonotonicityNot monotonic
2023-03-02T15:19:02.007301image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 2476
 
8.3%
19.77371993 230
 
0.8%
18.08314132 75
 
0.3%
27.94637722 75
 
0.3%
17.80449381 72
 
0.2%
50.009999 51
 
0.2%
19.92485885 50
 
0.2%
49 42
 
0.1%
-14.14213562 29
 
0.1%
20.42057786 28
 
0.1%
Other values (22336) 26837
89.6%
ValueCountFrequency (%)
-264.1533645 1
< 0.1%
-259.8576533 1
< 0.1%
-182.6198237 1
< 0.1%
-173.2050808 1
< 0.1%
-161.9073809 1
< 0.1%
-157.1750616 1
< 0.1%
-157.1686992 1
< 0.1%
-151.5255754 1
< 0.1%
-136.4477922 1
< 0.1%
-134.4916354 1
< 0.1%
ValueCountFrequency (%)
991.9334655 1
< 0.1%
862.5375354 1
< 0.1%
819.4900854 1
< 0.1%
804.2207408 1
< 0.1%
790.2379389 1
< 0.1%
778.424049 1
< 0.1%
773.1713911 1
< 0.1%
766.0456905 1
< 0.1%
762.7424205 1
< 0.1%
760.0539455 1
< 0.1%

BILL_AMT3
Real number (ℝ)

Distinct22026
Distinct (%)73.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean166.88286
Minimum-396.56525
Maximum1289.9961
Zeros0
Zeros (%)0.0%
Negative655
Negative (%)2.2%
Memory size468.2 KiB
2023-03-02T15:19:02.968055image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum-396.56525
5-th percentile1
Q152.076866
median141.90137
Q3245.36096
95-th percentile433.47664
Maximum1289.9961
Range1686.5614
Interquartile range (IQR)193.28409

Descriptive statistics

Standard deviation138.86594
Coefficient of variation (CV)0.83211626
Kurtosis0.75948204
Mean166.88286
Median Absolute Deviation (MAD)94.954596
Skewness0.91312632
Sum5000645
Variance19283.75
MonotonicityNot monotonic
2023-03-02T15:19:04.094849image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 2840
 
9.5%
19.77371993 274
 
0.9%
27.94637722 74
 
0.2%
18.08314132 63
 
0.2%
17.80449381 62
 
0.2%
19.92485885 47
 
0.2%
50.009999 40
 
0.1%
49 39
 
0.1%
20.42057786 29
 
0.1%
14.17744688 27
 
0.1%
Other values (22016) 26470
88.3%
ValueCountFrequency (%)
-396.5652531 1
< 0.1%
-248.0040322 1
< 0.1%
-214.7719721 1
< 0.1%
-184.5020325 1
< 0.1%
-159.5086205 1
< 0.1%
-157.1686992 1
< 0.1%
-142.5482374 1
< 0.1%
-133.0638944 1
< 0.1%
-126.1348485 1
< 0.1%
-125.0639836 1
< 0.1%
ValueCountFrequency (%)
1289.996124 1
< 0.1%
924.7091435 1
< 0.1%
832.5454943 1
< 0.1%
830.4480718 1
< 0.1%
830.4384384 1
< 0.1%
795.0106918 1
< 0.1%
772.9269047 1
< 0.1%
760.9020962 1
< 0.1%
760.2354898 1
< 0.1%
759.6156923 1
< 0.1%

BILL_AMT4
Real number (ℝ)

Distinct21548
Distinct (%)71.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean159.30609
Minimum-412.31056
Maximum944.23885
Zeros0
Zeros (%)0.0%
Negative675
Negative (%)2.3%
Memory size468.2 KiB
2023-03-02T15:19:05.139601image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum-412.31056
5-th percentile1
Q148.590122
median138.13761
Q3233.67071
95-th percentile417.69702
Maximum944.23885
Range1356.5494
Interquartile range (IQR)185.08059

Descriptive statistics

Standard deviation134.20123
Coefficient of variation (CV)0.84241118
Kurtosis0.726649
Mean159.30609
Median Absolute Deviation (MAD)91.505589
Skewness0.92365353
Sum4773606.9
Variance18009.97
MonotonicityNot monotonic
2023-03-02T15:19:06.260475image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 3165
 
10.6%
19.77371993 245
 
0.8%
27.94637722 101
 
0.3%
17.80449381 68
 
0.2%
18.08314132 62
 
0.2%
19.92485885 43
 
0.1%
49 39
 
0.1%
12.28820573 39
 
0.1%
50.009999 34
 
0.1%
20.42057786 33
 
0.1%
Other values (21538) 26136
87.2%
ValueCountFrequency (%)
-412.3105626 1
< 0.1%
-285.191164 1
< 0.1%
-255.2782795 1
< 0.1%
-224.9799991 1
< 0.1%
-215.9328599 1
< 0.1%
-185.7498318 1
< 0.1%
-165.8010856 1
< 0.1%
-155.8941949 1
< 0.1%
-148.687592 1
< 0.1%
-142.5482374 1
< 0.1%
ValueCountFrequency (%)
944.2388469 1
< 0.1%
840.752639 1
< 0.1%
792.9060474 1
< 0.1%
785.3897122 1
< 0.1%
756.839481 1
< 0.1%
754.3440859 1
< 0.1%
752.1103642 1
< 0.1%
750.6956774 1
< 0.1%
740.2844048 1
< 0.1%
736.6505277 1
< 0.1%

BILL_AMT5
Real number (ℝ)

Distinct21010
Distinct (%)70.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean152.46005
Minimum-285.19116
Maximum962.89771
Zeros0
Zeros (%)0.0%
Negative655
Negative (%)2.2%
Memory size468.2 KiB
2023-03-02T15:19:07.402802image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum-285.19116
5-th percentile1
Q142.284749
median134.6514
Q3224.16066
95-th percentile407.19357
Maximum962.89771
Range1248.0889
Interquartile range (IQR)181.87591

Descriptive statistics

Standard deviation131.10028
Coefficient of variation (CV)0.85989923
Kurtosis0.73117903
Mean152.46005
Median Absolute Deviation (MAD)90.959297
Skewness0.93757846
Sum4568465.5
Variance17187.284
MonotonicityNot monotonic
2023-03-02T15:19:08.494838image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 3476
 
11.6%
19.77371993 234
 
0.8%
27.94637722 94
 
0.3%
17.80449381 79
 
0.3%
18.08314132 62
 
0.2%
12.28820573 58
 
0.2%
19.92485885 46
 
0.2%
49 39
 
0.1%
50.009999 37
 
0.1%
20.42057786 36
 
0.1%
Other values (21000) 25804
86.1%
ValueCountFrequency (%)
-285.191164 1
< 0.1%
-247.733728 1
< 0.1%
-230.2324912 1
< 0.1%
-215.9328599 1
< 0.1%
-193.8917224 1
< 0.1%
-190.1473113 1
< 0.1%
-174.5880866 1
< 0.1%
-168.330033 1
< 0.1%
-151.6673993 1
< 0.1%
-144.0590157 1
< 0.1%
ValueCountFrequency (%)
962.89771 1
< 0.1%
907.4915978 1
< 0.1%
766.2036283 1
< 0.1%
742.7671237 1
< 0.1%
740.1898405 1
< 0.1%
728.4730606 1
< 0.1%
724.0966786 1
< 0.1%
718.4288413 1
< 0.1%
717.0181309 1
< 0.1%
712.8912961 1
< 0.1%

BILL_AMT6
Real number (ℝ)

Distinct20604
Distinct (%)68.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean147.81452
Minimum-582.75467
Maximum980.6452
Zeros0
Zeros (%)0.0%
Negative688
Negative (%)2.3%
Memory size468.2 KiB
2023-03-02T15:19:09.401644image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum-582.75467
5-th percentile1
Q135.538711
median130.86252
Q3221.93017
95-th percentile402.409
Maximum980.6452
Range1563.3999
Interquartile range (IQR)186.39146

Descriptive statistics

Standard deviation131.18923
Coefficient of variation (CV)0.88752602
Kurtosis0.72072168
Mean147.81452
Median Absolute Deviation (MAD)93.249056
Skewness0.92004715
Sum4429262
Variance17210.614
MonotonicityNot monotonic
2023-03-02T15:19:10.127218image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 3990
 
13.3%
19.77371993 206
 
0.7%
27.94637722 86
 
0.3%
12.28820573 78
 
0.3%
17.80449381 77
 
0.3%
18.08314132 56
 
0.2%
19.92485885 44
 
0.1%
20.42057786 36
 
0.1%
-4.242640687 33
 
0.1%
49 32
 
0.1%
Other values (20594) 25327
84.5%
ValueCountFrequency (%)
-582.7546654 1
< 0.1%
-457.2209532 1
< 0.1%
-388.5267044 1
< 0.1%
-307.6117683 1
< 0.1%
-271.8363478 1
< 0.1%
-238.8723508 1
< 0.1%
-226.8104936 1
< 0.1%
-226.2366018 1
< 0.1%
-215.9328599 1
< 0.1%
-213.8550911 1
< 0.1%
ValueCountFrequency (%)
980.6451958 1
< 0.1%
836.6271571 1
< 0.1%
754.0815606 1
< 0.1%
726.4378845 1
< 0.1%
726.3380756 1
< 0.1%
717.6182829 1
< 0.1%
716.79774 1
< 0.1%
715.4760653 1
< 0.1%
708.0755609 1
< 0.1%
706.4708062 1
< 0.1%

PAY_AMT1
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct7943
Distinct (%)26.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.6368885
Minimum0
Maximum13.680324
Zeros5218
Zeros (%)17.4%
Negative0
Negative (%)0.0%
Memory size468.2 KiB
2023-03-02T15:19:11.120394image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q16.9087548
median7.6511202
Q38.5189916
95-th percentile9.822722
Maximum13.680324
Range13.680324
Interquartile range (IQR)1.6102368

Descriptive statistics

Standard deviation3.2451887
Coefficient of variation (CV)0.48896236
Kurtosis0.28541881
Mean6.6368885
Median Absolute Deviation (MAD)0.866273
Skewness-1.2975552
Sum198874.36
Variance10.531249
MonotonicityNot monotonic
2023-03-02T15:19:12.039603image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5218
 
17.4%
7.601402335 1363
 
4.5%
8.006700845 891
 
3.0%
8.517393171 698
 
2.3%
7.313886832 507
 
1.7%
8.294299609 426
 
1.4%
9.210440367 401
 
1.3%
6.908754779 365
 
1.2%
7.824445931 298
 
1.0%
8.699681401 294
 
1.0%
Other values (7933) 19504
65.1%
ValueCountFrequency (%)
0 5218
17.4%
0.6931471806 9
 
< 0.1%
1.098612289 14
 
< 0.1%
1.386294361 15
 
0.1%
1.609437912 18
 
0.1%
1.791759469 12
 
< 0.1%
1.945910149 15
 
0.1%
2.079441542 9
 
< 0.1%
2.197224577 8
 
< 0.1%
2.302585093 7
 
< 0.1%
ValueCountFrequency (%)
13.68032408 1
< 0.1%
13.13231569 1
< 0.1%
13.10899238 1
< 0.1%
12.95726229 1
< 0.1%
12.91168432 1
< 0.1%
12.81638155 1
< 0.1%
12.68545404 1
< 0.1%
12.62746359 1
< 0.1%
12.61818561 1
< 0.1%
12.61167108 1
< 0.1%

PAY_AMT2
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct7899
Distinct (%)26.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.5694592
Minimum0
Maximum14.336837
Zeros5365
Zeros (%)17.9%
Negative0
Negative (%)0.0%
Memory size468.2 KiB
2023-03-02T15:19:12.864936image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q16.7464121
median7.6063874
Q38.5173932
95-th percentile9.8538665
Maximum14.336837
Range14.336837
Interquartile range (IQR)1.770981

Descriptive statistics

Standard deviation3.2738568
Coefficient of variation (CV)0.49834495
Kurtosis0.16290234
Mean6.5694592
Median Absolute Deviation (MAD)0.91100578
Skewness-1.2447624
Sum196853.85
Variance10.718138
MonotonicityNot monotonic
2023-03-02T15:19:13.689887image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5365
 
17.9%
7.601402335 1290
 
4.3%
8.006700845 857
 
2.9%
8.517393171 717
 
2.4%
6.908754779 594
 
2.0%
7.313886832 521
 
1.7%
8.294299609 410
 
1.4%
9.210440367 318
 
1.1%
8.699681401 283
 
0.9%
7.824445931 251
 
0.8%
Other values (7889) 19359
64.6%
ValueCountFrequency (%)
0 5365
17.9%
0.6931471806 15
 
0.1%
1.098612289 20
 
0.1%
1.386294361 18
 
0.1%
1.609437912 11
 
< 0.1%
1.791759469 25
 
0.1%
1.945910149 8
 
< 0.1%
2.079441542 12
 
< 0.1%
2.197224577 9
 
< 0.1%
2.302585093 6
 
< 0.1%
ValueCountFrequency (%)
14.33683686 1
< 0.1%
14.02015037 1
< 0.1%
14.01064304 1
< 0.1%
13.83973184 1
< 0.1%
13.27158479 1
< 0.1%
12.93736544 1
< 0.1%
12.90172668 1
< 0.1%
12.86908788 1
< 0.1%
12.86159324 1
< 0.1%
12.86096485 1
< 0.1%

PAY_AMT3
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct7518
Distinct (%)25.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.2896687
Minimum0
Maximum13.705741
Zeros5937
Zeros (%)19.8%
Negative0
Negative (%)0.0%
Memory size468.2 KiB
2023-03-02T15:19:14.770973image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15.9687076
median7.4983159
Q38.4147174
95-th percentile9.7758587
Maximum13.705741
Range13.705741
Interquartile range (IQR)2.4460098

Descriptive statistics

Standard deviation3.3455667
Coefficient of variation (CV)0.53191461
Kurtosis-0.24243414
Mean6.2896687
Median Absolute Deviation (MAD)1.0190773
Skewness-1.0834996
Sum188469.92
Variance11.192816
MonotonicityNot monotonic
2023-03-02T15:19:15.896857image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5937
 
19.8%
7.601402335 1285
 
4.3%
6.908754779 1103
 
3.7%
8.006700845 870
 
2.9%
8.517393171 721
 
2.4%
7.313886832 490
 
1.6%
8.294299609 381
 
1.3%
9.210440367 312
 
1.0%
7.090909822 243
 
0.8%
8.699681401 241
 
0.8%
Other values (7508) 18382
61.3%
ValueCountFrequency (%)
0 5937
19.8%
0.6931471806 13
 
< 0.1%
1.098612289 19
 
0.1%
1.386294361 14
 
< 0.1%
1.609437912 15
 
0.1%
1.791759469 18
 
0.1%
1.945910149 14
 
< 0.1%
2.079441542 18
 
0.1%
2.197224577 10
 
< 0.1%
2.302585093 12
 
< 0.1%
ValueCountFrequency (%)
13.70574145 1
< 0.1%
13.69790201 1
< 0.1%
13.13868938 1
< 0.1%
12.94225297 1
< 0.1%
12.90164937 1
< 0.1%
12.89192579 1
< 0.1%
12.84918626 1
< 0.1%
12.82589347 1
< 0.1%
12.76396123 1
< 0.1%
12.74915827 1
< 0.1%

PAY_AMT4
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6937
Distinct (%)23.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.0839568
Minimum0
Maximum13.339088
Zeros6377
Zeros (%)21.3%
Negative0
Negative (%)0.0%
Memory size468.2 KiB
2023-03-02T15:19:16.722346image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15.7071103
median7.3138868
Q38.2982906
95-th percentile9.6827162
Maximum13.339088
Range13.339088
Interquartile range (IQR)2.5911804

Descriptive statistics

Standard deviation3.3929404
Coefficient of variation (CV)0.55768648
Kurtosis-0.49248166
Mean6.0839568
Median Absolute Deviation (MAD)1.2035063
Skewness-0.96987039
Sum182305.77
Variance11.512045
MonotonicityNot monotonic
2023-03-02T15:19:17.906013image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 6377
 
21.3%
6.908754779 1394
 
4.7%
7.601402335 1214
 
4.1%
8.006700845 887
 
3.0%
8.517393171 810
 
2.7%
7.313886832 441
 
1.5%
8.294299609 402
 
1.3%
9.210440367 341
 
1.1%
7.824445931 259
 
0.9%
6.216606101 258
 
0.9%
Other values (6927) 17582
58.7%
ValueCountFrequency (%)
0 6377
21.3%
0.6931471806 22
 
0.1%
1.098612289 22
 
0.1%
1.386294361 13
 
< 0.1%
1.609437912 20
 
0.1%
1.791759469 12
 
< 0.1%
1.945910149 16
 
0.1%
2.079441542 11
 
< 0.1%
2.197224577 7
 
< 0.1%
2.302585093 9
 
< 0.1%
ValueCountFrequency (%)
13.33908797 1
< 0.1%
13.17855088 1
< 0.1%
13.11634732 1
< 0.1%
12.97648406 1
< 0.1%
12.89933732 1
< 0.1%
12.7122545 1
< 0.1%
12.70982229 1
< 0.1%
12.6761044 1
< 0.1%
12.65426194 1
< 0.1%
12.5878016 1
< 0.1%

PAY_AMT5
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6897
Distinct (%)23.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.0383791
Minimum0
Maximum12.963438
Zeros6672
Zeros (%)22.3%
Negative0
Negative (%)0.0%
Memory size468.2 KiB
2023-03-02T15:19:19.102995image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15.5683445
median7.3138868
Q38.3047423
95-th percentile9.6804065
Maximum12.963438
Range12.963438
Interquartile range (IQR)2.7363978

Descriptive statistics

Standard deviation3.4405253
Coefficient of variation (CV)0.56977629
Kurtosis-0.58900373
Mean6.0383791
Median Absolute Deviation (MAD)1.2035063
Skewness-0.94209151
Sum180940.03
Variance11.837214
MonotonicityNot monotonic
2023-03-02T15:19:19.890611image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 6672
 
22.3%
6.908754779 1340
 
4.5%
7.601402335 1323
 
4.4%
8.006700845 947
 
3.2%
8.517393171 814
 
2.7%
7.313886832 426
 
1.4%
8.294299609 401
 
1.3%
9.210440367 343
 
1.1%
6.216606101 250
 
0.8%
8.699681401 247
 
0.8%
Other values (6887) 17202
57.4%
ValueCountFrequency (%)
0 6672
22.3%
0.6931471806 21
 
0.1%
1.098612289 13
 
< 0.1%
1.386294361 13
 
< 0.1%
1.609437912 12
 
< 0.1%
1.791759469 9
 
< 0.1%
1.945910149 7
 
< 0.1%
2.079441542 9
 
< 0.1%
2.197224577 6
 
< 0.1%
2.302585093 6
 
< 0.1%
ValueCountFrequency (%)
12.96343798 1
< 0.1%
12.94321518 1
< 0.1%
12.86894617 1
< 0.1%
12.84599836 1
< 0.1%
12.71289326 1
< 0.1%
12.7122545 1
< 0.1%
12.70982229 1
< 0.1%
12.697379 1
< 0.1%
12.66690308 1
< 0.1%
12.64476619 1
< 0.1%

PAY_AMT6
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6939
Distinct (%)23.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.9389026
Minimum0
Maximum13.178114
Zeros7142
Zeros (%)23.8%
Negative0
Negative (%)0.0%
Memory size468.2 KiB
2023-03-02T15:19:20.843762image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14.8828019
median7.3138868
Q38.2942996
95-th percentile9.763386
Maximum13.178114
Range13.178114
Interquartile range (IQR)3.4114977

Descriptive statistics

Standard deviation3.524975
Coefficient of variation (CV)0.5935398
Kurtosis-0.75895716
Mean5.9389026
Median Absolute Deviation (MAD)1.2035063
Skewness-0.8580643
Sum177959.21
Variance12.425449
MonotonicityNot monotonic
2023-03-02T15:19:21.921782image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 7142
23.8%
6.908754779 1299
 
4.3%
7.601402335 1295
 
4.3%
8.006700845 914
 
3.1%
8.517393171 808
 
2.7%
7.313886832 439
 
1.5%
8.294299609 411
 
1.4%
9.210440367 356
 
1.2%
6.216606101 247
 
0.8%
8.699681401 220
 
0.7%
Other values (6929) 16834
56.2%
ValueCountFrequency (%)
0 7142
23.8%
0.6931471806 20
 
0.1%
1.098612289 9
 
< 0.1%
1.386294361 14
 
< 0.1%
1.609437912 12
 
< 0.1%
1.791759469 7
 
< 0.1%
1.945910149 6
 
< 0.1%
2.079441542 5
 
< 0.1%
2.197224577 6
 
< 0.1%
2.302585093 7
 
< 0.1%
ValueCountFrequency (%)
13.17811402 1
< 0.1%
13.17522903 1
< 0.1%
13.00132956 1
< 0.1%
12.95276296 1
< 0.1%
12.90793425 1
< 0.1%
12.84000312 1
< 0.1%
12.82798158 1
< 0.1%
12.76934745 1
< 0.1%
12.75215151 1
< 0.1%
12.63785831 1
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size468.2 KiB
0
23335 
1
6630 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters29965
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 23335
77.9%
1 6630
 
22.1%

Length

2023-03-02T15:19:22.686678image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-02T15:19:23.360952image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 23335
77.9%
1 6630
 
22.1%

Most occurring characters

ValueCountFrequency (%)
0 23335
77.9%
1 6630
 
22.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 29965
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 23335
77.9%
1 6630
 
22.1%

Most occurring scripts

ValueCountFrequency (%)
Common 29965
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 23335
77.9%
1 6630
 
22.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 29965
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 23335
77.9%
1 6630
 
22.1%

Interactions

2023-03-02T15:18:25.696456image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:15:51.857179image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:15:57.850023image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:16:04.937155image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:16:12.498788image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:16:20.811854image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:16:28.358734image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:16:35.049202image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:16:42.054097image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:16:49.154941image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:16:57.381930image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:17:04.657206image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:17:11.464384image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:17:18.514370image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:17:25.588474image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:17:32.622655image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:17:40.328741image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:17:46.143931image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:17:52.097576image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:18:03.276595image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:18:16.267752image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:18:26.161623image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:15:52.125166image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:15:58.124552image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:16:05.205657image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:16:12.897532image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:16:21.087132image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:16:28.645783image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:16:35.330263image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:16:42.344679image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:16:49.420848image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:16:57.844008image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:17:04.972516image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:17:11.751549image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:17:18.903873image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:17:25.880165image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:17:33.459314image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:17:40.605875image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:17:46.472676image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:17:52.360700image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:18:05.209001image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:18:16.590029image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:18:26.592579image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:15:52.393825image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:15:58.410027image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:16:05.496448image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:16:13.294689image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:16:21.369138image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:16:28.939363image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:16:35.619779image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:16:42.775303image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:16:49.688868image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:16:58.381340image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:17:05.317454image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:17:12.054666image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:17:19.319624image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:17:26.169944image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:17:33.918426image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:17:40.881883image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:17:46.824593image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:17:52.630578image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:18:06.240408image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:18:16.962276image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:18:26.984455image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:15:52.655704image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:15:58.688132image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:16:05.784714image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:16:13.608766image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:16:21.663914image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:16:29.282915image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:16:35.956351image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:16:43.182022image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:16:49.972332image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:16:58.724610image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:17:05.652729image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:17:12.353321image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:17:19.775651image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:17:26.452959image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:17:34.370733image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:17:41.149660image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:17:47.135204image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:17:52.896788image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:18:07.627997image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:18:17.275956image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:18:27.377050image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:15:52.899076image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:15:58.949162image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:16:06.056935image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:16:13.938640image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:16:21.966670image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:16:29.568774image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:16:36.225319image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:16:43.479940image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:16:50.362026image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:16:59.032070image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:17:05.983526image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:17:12.643458image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:17:20.072758image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:17:26.716178image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:17:34.729605image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:17:41.390947image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:17:47.429364image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:17:53.154237image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:18:08.355946image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:18:17.657136image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:18:28.533484image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:15:53.167624image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:15:59.213327image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:16:06.363329image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:16:14.405171image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
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2023-03-02T15:17:50.141831image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:17:56.000408image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:18:13.491040image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:18:22.502460image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:18:31.896376image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:15:56.070658image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:16:03.269272image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:16:09.854723image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:16:18.957170image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:16:26.248914image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:16:32.895090image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:16:40.319044image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:16:47.444031image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:16:54.835869image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:17:02.704431image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:17:09.602103image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:17:16.280537image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:17:23.361709image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:17:30.699208image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:17:38.406576image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:17:44.300198image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:17:50.438094image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:17:57.007600image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:18:13.835189image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:18:22.932460image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:18:32.242523image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:15:56.396703image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:16:03.550876image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:16:10.329209image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:16:19.256395image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:16:26.871102image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:16:33.202909image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:16:40.612716image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:16:47.735831image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:16:55.229729image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:17:03.026922image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:17:09.942055image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:17:16.702902image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:17:23.712814image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:17:30.999530image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:17:38.717856image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:17:44.573301image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:17:50.711546image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:17:57.563703image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:18:14.168539image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:18:23.426149image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:18:32.540720image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:15:56.680923image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:16:03.830973image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:16:10.779927image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:16:19.565880image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:16:27.170742image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:16:33.576476image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:16:40.904955image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:16:48.030895image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:16:55.547506image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:17:03.350344image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:17:10.265908image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:17:17.139794image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:17:24.120952image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:17:31.298115image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:17:39.013953image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:17:44.847245image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:17:50.990719image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:17:58.036747image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:18:14.542298image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:18:23.906535image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:18:32.838515image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:15:56.961857image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:16:04.105275image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:16:11.251070image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:16:19.867204image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:16:27.479656image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:16:33.971261image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:16:41.195386image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:16:48.323370image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:16:55.835362image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:17:03.680385image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:17:10.567440image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:17:17.541062image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:17:24.452916image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:17:31.591072image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:17:39.323212image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:17:45.118172image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:17:51.284473image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:17:58.672943image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:18:14.944300image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:18:24.380510image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:18:33.125479image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:15:57.234476image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:16:04.389183image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:16:11.718287image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:16:20.188800image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:16:27.789401image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:16:34.335948image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:16:41.496352image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:16:48.606434image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:16:56.203459image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:17:04.010402image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:17:10.867909image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:17:17.911810image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:17:24.853802image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:17:32.002543image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:17:39.614857image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:17:45.406222image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:17:51.560540image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:17:59.214087image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:18:15.460589image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:18:24.821030image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:18:33.432048image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:15:57.498924image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:16:04.657430image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:16:12.152276image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:16:20.519297image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:16:28.067606image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:16:34.688339image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:16:41.770700image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:16:48.881634image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:16:56.508483image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:17:04.337104image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:17:11.168485image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:17:18.219966image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:17:25.212168image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:17:32.323508image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:17:39.975911image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:17:45.745977image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:17:51.833539image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:18:00.183206image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:18:15.888225image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T15:18:25.271273image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Correlations

2023-03-02T15:19:24.035035image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
LIMIT_BALEDUCATIONAGEPAY_0PAY_2PAY_3PAY_4PAY_5PAY_6BILL_AMT1BILL_AMT2BILL_AMT3BILL_AMT4BILL_AMT5BILL_AMT6PAY_AMT1PAY_AMT2PAY_AMT3PAY_AMT4PAY_AMT5PAY_AMT6SEXMARRIAGEdefault.payment.next.month
LIMIT_BAL1.000-0.2630.186-0.297-0.342-0.331-0.308-0.285-0.2640.0550.0500.0620.0740.0820.0890.2740.2800.2860.2850.2950.3190.1340.0730.173
EDUCATION-0.2631.0000.1590.1330.1680.1610.1510.1360.1230.0940.0920.0800.0680.0590.055-0.043-0.048-0.043-0.044-0.051-0.0540.0290.1140.072
AGE0.1860.1591.000-0.064-0.084-0.084-0.081-0.083-0.0760.0010.0010.002-0.004-0.001-0.0000.0330.0440.0330.0400.0380.0390.0910.2860.048
PAY_0-0.2970.133-0.0641.0000.6290.5500.5180.4880.4650.3160.3310.3160.3080.3000.290-0.098-0.063-0.054-0.034-0.026-0.0450.0590.0360.422
PAY_2-0.3420.168-0.0840.6291.0000.7990.7120.6730.6340.5700.5500.5170.4960.4770.4580.0180.0810.0850.0930.0970.0800.0710.0340.340
PAY_3-0.3310.161-0.0840.5500.7991.0000.8000.7180.6700.5230.5870.5560.5300.5060.4830.2140.0340.1010.1170.1220.0960.0670.0320.294
PAY_4-0.3080.151-0.0810.5180.7120.8001.0000.8220.7310.5110.5570.6180.5920.5600.5320.1830.2440.0670.1420.1600.1410.0630.0350.278
PAY_5-0.2850.136-0.0830.4880.6730.7180.8221.0000.8200.4980.5360.5860.6490.6170.5780.1730.2200.2580.1040.1830.1700.0560.0330.269
PAY_6-0.2640.123-0.0760.4650.6340.6700.7310.8201.0000.4870.5220.5600.6050.6670.6290.1760.1980.2360.2820.1390.1960.0470.0300.249
BILL_AMT10.0550.0940.0010.3160.5700.5230.5110.4980.4871.0000.9110.8570.8070.7690.7340.5010.4710.4390.4410.4240.4090.0460.0240.039
BILL_AMT20.0500.0920.0010.3310.5500.5870.5570.5360.5220.9111.0000.9080.8480.8030.7650.6350.4960.4670.4600.4480.4280.0530.0320.036
BILL_AMT30.0620.0800.0020.3160.5170.5560.6180.5860.5600.8570.9081.0000.9030.8480.8040.5490.6370.4910.4870.4760.4570.0400.0260.032
BILL_AMT40.0740.068-0.0040.3080.4960.5300.5920.6490.6050.8070.8480.9031.0000.9030.8480.5110.5540.6330.5060.5030.4800.0410.0270.032
BILL_AMT50.0820.059-0.0010.3000.4770.5060.5600.6170.6670.7690.8030.8480.9031.0000.9020.4810.5140.5480.6460.5240.5080.0380.0360.034
BILL_AMT60.0890.055-0.0000.2900.4580.4830.5320.5780.6290.7340.7650.8040.8480.9021.0000.4550.4860.5180.5690.6660.5280.0270.0220.015
PAY_AMT10.274-0.0430.033-0.0980.0180.2140.1830.1730.1760.5010.6350.5490.5110.4810.4551.0000.5110.5180.4850.4670.4540.0280.0270.176
PAY_AMT20.280-0.0480.044-0.0630.0810.0340.2440.2200.1980.4710.4960.6370.5540.5140.4860.5111.0000.5150.5190.4960.4900.0280.0280.156
PAY_AMT30.286-0.0430.033-0.0540.0850.1010.0670.2580.2360.4390.4670.4910.6330.5480.5180.5180.5151.0000.5150.5330.5050.0430.0190.146
PAY_AMT40.285-0.0440.040-0.0340.0930.1170.1420.1040.2820.4410.4600.4870.5060.6460.5690.4850.5190.5151.0000.5330.5460.0300.0250.133
PAY_AMT50.295-0.0510.038-0.0260.0970.1220.1600.1830.1390.4240.4480.4760.5030.5240.6660.4670.4960.5330.5331.0000.5480.0300.0230.119
PAY_AMT60.319-0.0540.039-0.0450.0800.0960.1410.1700.1960.4090.4280.4570.4800.5080.5280.4540.4900.5050.5460.5481.0000.0420.0220.121
SEX0.1340.0290.0910.0590.0710.0670.0630.0560.0470.0460.0530.0400.0410.0380.0270.0280.0280.0430.0300.0300.0421.0000.0320.039
MARRIAGE0.0730.1140.2860.0360.0340.0320.0350.0330.0300.0240.0320.0260.0270.0360.0220.0270.0280.0190.0250.0230.0220.0321.0000.033
default.payment.next.month0.1730.0720.0480.4220.3400.2940.2780.2690.2490.0390.0360.0320.0320.0340.0150.1760.1560.1460.1330.1190.1210.0390.0331.000

Missing values

2023-03-02T15:18:34.129015image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-03-02T15:18:35.728631image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

LIMIT_BALSEXEDUCATIONMARRIAGEAGEPAY_0PAY_2PAY_3PAY_4PAY_5PAY_6BILL_AMT1BILL_AMT2BILL_AMT3BILL_AMT4BILL_AMT5BILL_AMT6PAY_AMT1PAY_AMT2PAY_AMT3PAY_AMT4PAY_AMT5PAY_AMT6default.payment.next.month
ID
19.9035382212422-1-1-2-262.56196955.70457826.2678511.0000001.0000001.0000000.0000006.5366920.0000000.0000000.0000000.0000001
211.69525522226-12000251.79768341.54515651.79768357.21013958.78775457.1139210.0000006.9087556.9087556.9087550.0000007.6014021
311.40757622234000000170.997076118.439858116.447413119.716331122.266103124.6996397.3258087.3138876.9087556.9087556.9087558.5173930
410.81979822137000000216.774076219.622403222.018017168.270615170.176379171.8953177.6014027.6108537.0909107.0039746.9754146.9087550
510.81979812157-10-100092.83318475.306042189.303988144.710055138.372685138.3184737.60140210.5100419.2104409.1050916.5366926.5220930
610.81979811237000000253.773521238.893282240.018749139.265933140.071410141.5097177.8244467.5043926.4892056.9087556.9087556.6858610
713.12236511229000000606.602011641.890956667.089199736.650528694.984892688.43663510.91510710.59666010.5453689.9154169.5288679.5303200
811.512935222230-1-100-1108.98165019.51922124.53568814.899664-12.60952023.8327515.9427996.4002570.0000006.3664707.4313007.3414840
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